NVIDIA
RTX 4080 Laptop 12GB
RTX 40 LaptopLaptopAda LovelaceMOBILECUDA
Operating mode
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Use this to bias workload recommendations toward responsiveness, background autonomy, lighter serving, or multi-GPU scale-out.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
About this GPU for AI
The RTX 4080 Laptop GPU offers 12 GB of GDDR6 at 432 GB/s bandwidth with 31 TFLOPS FP16 in a configurable 60–150W TGP. It is a strong mid-to-high laptop option for AI inference, fitting 7B models at FP16 with headroom and handling 13B models at Q4 comfortably. Compared to the desktop RTX 4080 (16 GB, 320W), it provides roughly 40–50% of sustained compute at a fraction of the power, with 4 GB less VRAM.
Beyond LLMs
AI Capability Matrix
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model | Detail |
|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | — |
| LLM Coding (30B) | Won’t fit | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
portablethermally-limitedlaptopada-lovelace
Specifications
Compute
FP1631 TFLOPS
INT8496 TOPS
ArchitectureAda Lovelace
Memory
VRAM12 GB
Bandwidth432 GB/s
General
FamilyRTX 40 Laptop
SegmentLaptop
InterconnectMOBILE
Compute PlatformCUDA
Key Features
12 GB GDDR6 VRAMAda Lovelace 4th-gen Tensor Cores with FP8 support31 TFLOPS FP16 / 496 INT8 TOPS432 GB/s memory bandwidth60–150W configurable TGPDLSS 3 with Frame Generation
For AI Workloads
Strengths
- 12 GB VRAM fits 7B models at FP16 and 13B models at Q4 with comfortable headroom
- 432 GB/s bandwidth provides solid decode throughput for a laptop GPU
- FP8 Tensor Cores enable efficient quantized inference on modern frameworks
- Good balance of VRAM, bandwidth, and power efficiency for a high-end AI laptop
Considerations
- 12 GB still falls short for 30B models — Q4 quantization required and will be slow
- Sustained performance at 60W Max-Q is significantly below the 150W Max-P ceiling
- Desktop RTX 4080 16GB offers 4 GB more VRAM and roughly 2x sustained compute
- Thin laptop designs may throttle under prolonged inference loads due to thermal limits
Ada Lovelace is NVIDIA's fourth-generation RTX architecture, manufactured on TSMC's custom 4N process. It introduces 4th-generation Tensor Cores with FP8 support, 3rd-generation ray tracing cores, and the Shader Execution Reordering (SER) engine for improved workload scheduling.
AI Relevance
FP8 Tensor Core operations provide a significant uplift for quantized LLM inference compared to Ampere's FP16-only Tensor Cores. DLSS 3 Frame Generation demonstrates the architecture's AI processing capabilities.
Process: TSMC 4NPlatform: CUDATensor Cores: Gen 4Precisions: FP32, FP16, BF16, FP8, INT8, INT4
Recommendations by Workload
Qwen 3.5 9B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 66.0 tok/s · 32K ctx · llama.cppEST.
Qwen 3.5 9B is a specialized fit for Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 66.0 tok/s · 32K ctx · llama.cppEST.
Just out of reach
Models you could run with an upgrade
High-quality models that need a bit more memory
30.5BTier 100Needs ~21.4 GB
397BTier 100Needs ~245.7 GB
123BTier 100Needs ~79.8 GB
1000BTier 100Needs ~615.8 GB
1000BTier 100Needs ~615.8 GB
Image & Video Generation
Diffusion Model Compatibility
24 of 52 models can generate images or video on your RTX 4080 Laptop 12GB
Upgrade paths
Upgrade from RTX 4080 Laptop 12GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
Frequently Asked Questions
12
GB
RTX 4080 Laptop 12GBCategory AvgMacBook Pro M3 Pro 18GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~10.3s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~46.4s per image |
| Image Gen (SD 3.5) | Won't fit | SD 3.5 Large FP16 | ~~56.7s per image |
| Video Short (25f) | Runs with offload | LTX Video 2B | ~~9s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~26.4s/frame |
CodeGeeX 4 9B is a specialized fit for Agentic Coding. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 67.1 tok/s · 116K ctx · llama.cppEST.
Qwen 3.5 9B matches Reasoning and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 66.0 tok/s · 32K ctx · llama.cppEST.
CodeGeeX 4 9B is viable for RAG, but is not the most specialized choice. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 67.1 tok/s · 116K ctx · llama.cppEST.
4B
6.7 GB
64 tok/s
54K ctx
Image
| MAGI-1Video | 256×256 | ~24.2s/frame | F |
Image models estimated at 1024×1024 (28 steps, FP16). Video models estimated at 768×512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.
Buying advice
Should you buy RTX 4080 Laptop 12GB for local AI?
Usable for local AI with limits
Can run 10 of 50 top models, mostly smaller ones. Larger models need heavy quantization or won't fit.
What will limit you first
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best upgrade itinerary
Unlocks 1 additional models that do not fit on the current setup.
Want more headroom? MacBook Pro M3 Pro 18GB (18.0 GB unified memory) is the next step up.
